No matter how well an AI pipeline is designed, it will always be limited by both quality and quantity of available data. However, real-world data rarely arrives nicely packaged and pre-normalized. In fact, it is probably safer to say that real-world data is the number one saboteur of existing machine learning applications. This talk will go over common data problems that have been seen in enterprise systems and the importance of curating data for pipelines of all varieties.
Colby Mainard is a machine learning engineer with more than five years of experience designing, deploying, and operating production ML systems, currently working as an AI/ML engineer at RiskScout on transaction anomaly detection, entity risk scoring, and network analysis across financial data. He holds a Master of Computer Science from Texas A&M University with a focus on artificial intelligence, machine learning, deep learning, and computer vision, along with a B.S. in Computer Science from the same institution with minors in business and cybersecurity. Earlier roles span sports analytics and cybersecurity, including computer vision pipelines for the NFL, NHL, MLB, and NBA at MVP, as well as Python and C++ data tooling for threat detection at Vectra. Outside of work he studies quantum computing, plays Dungeons and Dragons, enjoys learning about history, and shoots landscape photography.
